Provides posterior summary of country-specific Forecasts
Source:R/summary.R
summary.ForecastsPANEL.RdProvides posterior summary of the forecasts including their mean, standard deviations, as well as 5 and 95 percentiles.
Usage
# S3 method for class 'ForecastsPANEL'
summary(object, which_c, ...)Arguments
- object
an object of class
ForecastsPANELobtained using theforecast()function containing draws the predictive density.- which_c
a positive integer or a character string specifying the country for which the forecast should be plotted.
- ...
additional arguments affecting the summary produced.
Value
A list reporting the posterior mean, standard deviations, as well as 5 and 95 percentiles of the forecasts for each of the variables and forecast horizons.
Author
Tomasz Woźniak wozniak.tom@pm.me
Examples
data(ilo_dynamic_panel) # load the data
data(ilo_exogenous_variables) # load the exogenous variables
data(ilo_exogenous_forecasts) # load the exogenous forecast
set.seed(123)
# specify the model
specification = specify_bvarPANEL$new(ilo_dynamic_panel, exogenous = ilo_exogenous_variables)
burn_in = estimate(specification, 10) # run the burn-in
#> **************************************************|
#> bvarPANELs: Forecasting with Bayesian Hierarchical|
#> Panel Vector Autoregressions |
#> **************************************************|
#> Progress of the MCMC simulation for 10 draws
#> Every draw is saved via MCMC thinning
#> Press Esc to interrupt the computations
#> **************************************************|
posterior = estimate(burn_in, 10) # estimate the model
#> **************************************************|
#> bvarPANELs: Forecasting with Bayesian Hierarchical|
#> Panel Vector Autoregressions |
#> **************************************************|
#> Progress of the MCMC simulation for 10 draws
#> Every draw is saved via MCMC thinning
#> Press Esc to interrupt the computations
#> **************************************************|
# forecast 6 years ahead
predictive = forecast(posterior, 6, exogenous_forecast = ilo_exogenous_forecasts)
#> **************************************************|
#> bvarPANELs: Forecasting with Bayesian Hierarchical|
#> Panel Vector Autoregressions |
#> **************************************************|
#> Progress of sampling 10 draws from
#> the predictive density for 189 countries
#> Press Esc to interrupt the computations
#> **************************************************|
summary(predictive, which_c = "POL")
#> **************************************************|
#> bsvars: Bayesian Structural Vector Autoregressions|
#> **************************************************|
#> Posterior summary of forecasts |
#> **************************************************|
#> $variable1
#> mean sd 5% quantile 95% quantile
#> 1 27.21313 0.02408531 27.17925 27.24678
#> 2 27.26329 0.02871019 27.22304 27.30289
#> 3 27.30318 0.03550437 27.25307 27.34851
#> 4 27.32995 0.04919806 27.24869 27.37955
#> 5 27.36594 0.05946100 27.26911 27.43260
#> 6 27.38929 0.07180236 27.26397 27.45593
#>
#> $variable2
#> mean sd 5% quantile 95% quantile
#> 1 2.40783027 2.782698 -0.455143 6.627415
#> 2 1.24354630 4.155141 -4.184068 6.868560
#> 3 0.32535686 4.876128 -5.561055 7.804874
#> 4 -0.02924289 5.291247 -5.597902 9.022041
#> 5 -0.69981390 6.526451 -7.952230 10.394746
#> 6 -0.97105073 7.039195 -8.337951 11.059085
#>
#> $variable3
#> mean sd 5% quantile 95% quantile
#> 1 57.16358 1.355405 55.11561 58.91817
#> 2 57.91772 2.419252 54.78791 61.28129
#> 3 58.24847 2.906429 54.08480 61.98811
#> 4 58.65542 3.449756 53.53578 63.15353
#> 5 59.20002 4.235689 52.57040 64.33862
#> 6 59.35998 4.387566 52.35463 64.62865
#>
#> $variable4
#> mean sd 5% quantile 95% quantile
#> 1 58.59887 0.5862386 57.89950 59.48834
#> 2 58.76026 0.7260036 57.75559 59.62949
#> 3 58.58566 0.5770285 57.94311 59.47936
#> 4 58.83854 1.2021483 57.17111 60.42909
#> 5 59.04450 1.0844067 57.37556 60.27955
#> 6 59.04841 1.1259090 57.42927 60.35871
#>
# workflow with the pipe |>
############################################################
set.seed(123)
ilo_dynamic_panel |>
specify_bvarPANEL$new() |>
estimate(S = 10) |>
estimate(S = 20) |>
forecast(horizon = 2) |>
summary(which_c = "POL")
#> **************************************************|
#> bvarPANELs: Forecasting with Bayesian Hierarchical|
#> Panel Vector Autoregressions |
#> **************************************************|
#> Progress of the MCMC simulation for 10 draws
#> Every draw is saved via MCMC thinning
#> Press Esc to interrupt the computations
#> **************************************************|
#> **************************************************|
#> bvarPANELs: Forecasting with Bayesian Hierarchical|
#> Panel Vector Autoregressions |
#> **************************************************|
#> Progress of the MCMC simulation for 20 draws
#> Every draw is saved via MCMC thinning
#> Press Esc to interrupt the computations
#> **************************************************|
#> **************************************************|
#> bvarPANELs: Forecasting with Bayesian Hierarchical|
#> Panel Vector Autoregressions |
#> **************************************************|
#> Progress of sampling 20 draws from
#> the predictive density for 189 countries
#> Press Esc to interrupt the computations
#> **************************************************|
#> **************************************************|
#> bsvars: Bayesian Structural Vector Autoregressions|
#> **************************************************|
#> Posterior summary of forecasts |
#> **************************************************|
#> $variable1
#> mean sd 5% quantile 95% quantile
#> 1 27.20833 0.02292586 27.17483 27.24610
#> 2 27.24256 0.04282843 27.19347 27.31346
#>
#> $variable2
#> mean sd 5% quantile 95% quantile
#> 1 2.997496 2.163707 -0.2116465 6.849011
#> 2 2.916851 2.763951 -1.2962584 6.878897
#>
#> $variable3
#> mean sd 5% quantile 95% quantile
#> 1 57.07870 1.023951 55.62295 58.51290
#> 2 57.32834 1.476331 55.56382 59.33173
#>
#> $variable4
#> mean sd 5% quantile 95% quantile
#> 1 58.90945 0.4657256 58.26021 59.50532
#> 2 59.14215 0.6199194 58.12680 59.89916
#>
# conditional forecasting 6 years ahead conditioning on
# provided future values for the Gross Domestic Product
# growth rate
############################################################
data(ilo_conditional_forecasts) # load the conditional forecasts of dgdp
specification = specify_bvarPANEL$new(ilo_dynamic_panel) # specify the model
burn_in = estimate(specification, 10) # run the burn-in
#> **************************************************|
#> bvarPANELs: Forecasting with Bayesian Hierarchical|
#> Panel Vector Autoregressions |
#> **************************************************|
#> Progress of the MCMC simulation for 10 draws
#> Every draw is saved via MCMC thinning
#> Press Esc to interrupt the computations
#> **************************************************|
posterior = estimate(burn_in, 10) # estimate the model
#> **************************************************|
#> bvarPANELs: Forecasting with Bayesian Hierarchical|
#> Panel Vector Autoregressions |
#> **************************************************|
#> Progress of the MCMC simulation for 10 draws
#> Every draw is saved via MCMC thinning
#> Press Esc to interrupt the computations
#> **************************************************|
# forecast 6 years ahead
predictive = forecast(posterior, 6, conditional_forecast = ilo_conditional_forecasts)
#> **************************************************|
#> bvarPANELs: Forecasting with Bayesian Hierarchical|
#> Panel Vector Autoregressions |
#> **************************************************|
#> Progress of sampling 10 draws from
#> the predictive density for 189 countries
#> Press Esc to interrupt the computations
#> **************************************************|
summary(predictive, which_c = "POL")
#> **************************************************|
#> bsvars: Bayesian Structural Vector Autoregressions|
#> **************************************************|
#> Posterior summary of forecasts |
#> **************************************************|
#> $variable1
#> mean sd 5% quantile 95% quantile
#> 1 27.20126 0 27.20126 27.20126
#> 2 27.23543 0 27.23543 27.23543
#> 3 27.26786 0 27.26786 27.26786
#> 4 27.29738 0 27.29738 27.29738
#> 5 27.32664 0 27.32664 27.32664
#> 6 27.35630 0 27.35630 27.35630
#>
#> $variable2
#> mean sd 5% quantile 95% quantile
#> 1 2.973705 1.227861 1.1769236 4.507933
#> 2 2.635335 2.036479 0.6156404 5.906735
#> 3 1.999701 2.867749 -1.6137005 6.304514
#> 4 2.163981 3.533968 -3.0439007 6.899090
#> 5 2.129214 4.322081 -3.2777189 8.573412
#> 6 1.545401 4.708804 -4.6379531 8.521792
#>
#> $variable3
#> mean sd 5% quantile 95% quantile
#> 1 56.81392 0.8890254 55.73613 58.14816
#> 2 57.13281 1.0676520 55.86340 58.84989
#> 3 57.72602 1.3030436 55.97099 59.32381
#> 4 57.92416 1.5463865 55.63428 59.65459
#> 5 58.13497 1.8713576 55.16764 60.16334
#> 6 58.62843 2.2359815 55.00153 61.28963
#>
#> $variable4
#> mean sd 5% quantile 95% quantile
#> 1 58.59000 0.6263350 57.68068 59.22851
#> 2 58.75516 0.8354557 57.47867 59.67254
#> 3 59.04370 1.0282282 57.74988 60.50456
#> 4 59.36669 1.0053499 58.06295 60.77492
#> 5 59.58982 1.3874843 57.71137 61.37416
#> 6 59.74854 1.2670284 57.72361 61.17385
#>
# workflow with the pipe |>
############################################################
set.seed(123)
ilo_dynamic_panel |>
specify_bvarPANEL$new() |>
estimate(S = 10) |>
estimate(S = 20) |>
forecast(
horizon = 6,
conditional_forecast = ilo_conditional_forecasts
) |>
summary(which_c = "POL")
#> **************************************************|
#> bvarPANELs: Forecasting with Bayesian Hierarchical|
#> Panel Vector Autoregressions |
#> **************************************************|
#> Progress of the MCMC simulation for 10 draws
#> Every draw is saved via MCMC thinning
#> Press Esc to interrupt the computations
#> **************************************************|
#> **************************************************|
#> bvarPANELs: Forecasting with Bayesian Hierarchical|
#> Panel Vector Autoregressions |
#> **************************************************|
#> Progress of the MCMC simulation for 20 draws
#> Every draw is saved via MCMC thinning
#> Press Esc to interrupt the computations
#> **************************************************|
#> **************************************************|
#> bvarPANELs: Forecasting with Bayesian Hierarchical|
#> Panel Vector Autoregressions |
#> **************************************************|
#> Progress of sampling 20 draws from
#> the predictive density for 189 countries
#> Press Esc to interrupt the computations
#> **************************************************|
#> **************************************************|
#> bsvars: Bayesian Structural Vector Autoregressions|
#> **************************************************|
#> Posterior summary of forecasts |
#> **************************************************|
#> $variable1
#> mean sd 5% quantile 95% quantile
#> 1 27.20126 0 27.20126 27.20126
#> 2 27.23543 0 27.23543 27.23543
#> 3 27.26786 0 27.26786 27.26786
#> 4 27.29738 0 27.29738 27.29738
#> 5 27.32664 0 27.32664 27.32664
#> 6 27.35630 0 27.35630 27.35630
#>
#> $variable2
#> mean sd 5% quantile 95% quantile
#> 1 3.007303 1.188344 1.5491294 4.551334
#> 2 2.941612 1.411844 0.5280873 4.906943
#> 3 3.262896 1.177707 1.3276339 4.721227
#> 4 3.607827 1.851831 0.6632864 5.906053
#> 5 3.782638 2.074991 0.8245657 7.411908
#> 6 4.650123 2.701818 1.0467395 8.425201
#>
#> $variable3
#> mean sd 5% quantile 95% quantile
#> 1 56.87364 0.7397435 55.72259 58.08835
#> 2 57.11127 0.8710991 55.93056 58.67596
#> 3 57.25469 0.6118194 56.38072 58.11050
#> 4 57.26761 0.7616915 55.96153 58.59708
#> 5 57.23641 0.9094898 55.89276 58.62558
#> 6 56.84611 1.7019920 54.56678 59.33118
#>
#> $variable4
#> mean sd 5% quantile 95% quantile
#> 1 58.68079 0.6008518 57.77759 59.60783
#> 2 58.94663 0.9405458 57.85657 60.67330
#> 3 59.31390 0.7257944 58.33238 60.67594
#> 4 59.53580 0.9285039 58.16205 60.93998
#> 5 59.60022 0.8712144 58.28623 61.00937
#> 6 59.71798 1.0182805 58.28243 61.45111
#>